English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In recent years, long short-term memory neural networks (LSTMs) have beenapplied quite successfully to problems in handwritten text recognition.However, their strength is more located in handling sequences of variablelength than in handling geometric variability of the image patterns.Furthermore, the best results for LSTMs are often based on large-scale trainingof an ensemble of network instances. In this paper, an end-to-end convolutionalLSTM Neural Network is used to handle both geometric variation and sequencevariability. We show that high performances can be reached on a commonbenchmark set by using proper data augmentation for just five such networksusing a proper coding scheme and a proper voting scheme. The networks havesimilar architectures (Convolutional Neural Network (CNN): five layers,bidirectional LSTM (BiLSTM): three layers followed by a connectionist temporalclassification (CTC) processing step). The approach assumes differently-scaledinput images and different feature map sizes. Two datasets are used forevaluation of the performance of our algorithm: A standard benchmark RIMESdataset (French), and a historical handwritten dataset KdK (Dutch). Finalperformance obtained for the word-recognition test of RIMES was 96.6%, a clearimprovement over other state-of-the-art approaches. On the KdK dataset, ourapproach also shows good results. The proposed approach is deployed in the Monksearch engine for historical-handwriting collections.

A limited-size ensemble of homogeneous CNN/LSTMs for high-performance word classification